scholarly journals A Framework to Predict High-Resolution Spatiotemporal PM2.5 Distributions Using a Deep-Learning Model: A Case Study of Shijiazhuang, China

2020 ◽  
Vol 12 (17) ◽  
pp. 2825
Author(s):  
Guangyuan Zhang ◽  
Haiyue Lu ◽  
Jin Dong ◽  
Stefan Poslad ◽  
Runkui Li ◽  
...  
Keyword(s):  
High Resolution ◽  
Short Term Memory ◽  
Moving Average ◽  
Time Series Prediction ◽  
Score Function ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Estimation Model ◽  
Extreme Gradient Boosting ◽  
Selection Operator

Air-borne particulate matter, PM2.5 (PM having a diameter of less than 2.5 micrometers), has aroused widespread concern and is a core indicator of severe air pollution in many cities globally. In our study, we present a validated framework to predict the daily PM2.5 distributions, exemplified by a use case of Shijiazhuang City, China, based on daily aerosol optical depth (AOD) datasets. The framework involves obtaining the high-resolution spatiotemporal AOD distributions, estimation of the spatial distributions of PM2.5 and the prediction of these based on a convolutional long short-term memory (ConvLSTM) model. In the estimation part, the eXtreme gradient boosting (XGBoost) model has been determined as the estimation model with the lowest root mean square error (RMSE) of 32.86 µg/m3 and the highest coefficient of determination regression score function (R2) of 0.71, compared to other common models used as a baseline for comparison (linear, ridge, least absolute shrinkage and selection operator (LASSO) and cubist). For the prediction part, after validation and comparison with a seasonal autoregressive integrated moving average (SARIMA), which is a traditional time-series prediction model, in both time and space, the ConvLSTM gives a more accurate performance for the prediction, with a total average prediction RMSE of 14.94 µg/m3 compared to SARIMA’s 17.41 µg/m3. Furthermore, ConvLSTM is more stable and with less fluctuations for the prediction of PM2.5 in time, and it can also eliminate better the spatial predicted errors compared to SARIMA.

scholarly journals A Machine Learning Method for Predicting Vegetation Indices in China

2021 ◽  
Vol 13 (6) ◽  
pp. 1147
Author(s):  
Xiangqian Li ◽  
Wenping Yuan ◽  
Wenjie Dong
Keyword(s):  
Machine Learning ◽  
Growing Season ◽  
Crop Growth ◽  
Spatiotemporal Distribution ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Severe Drought ◽  
Vegetation Growth ◽  
Extreme Gradient Boosting ◽  
Boosting Method

To forecast the terrestrial carbon cycle and monitor food security, vegetation growth must be accurately predicted; however, current process-based ecosystem and crop-growth models are limited in their effectiveness. This study developed a machine learning model using the extreme gradient boosting method to predict vegetation growth throughout the growing season in China from 2001 to 2018. The model used satellite-derived vegetation data for the first month of each growing season, CO2 concentration, and several meteorological factors as data sources for the explanatory variables. Results showed that the model could reproduce the spatiotemporal distribution of vegetation growth as represented by the satellite-derived normalized difference vegetation index (NDVI). The predictive error for the growing season NDVI was less than 5% for more than 98% of vegetated areas in China; the model represented seasonal variations in NDVI well. The coefficient of determination (R2) between the monthly observed and predicted NDVI was 0.83, and more than 69% of vegetated areas had an R2 > 0.8. The effectiveness of the model was examined for a severe drought year (2009), and results showed that the model could reproduce the spatiotemporal distribution of NDVI even under extreme conditions. This model provides an alternative method for predicting vegetation growth and has great potential for monitoring vegetation dynamics and crop growth.

scholarly journals Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4772 ◽  
Author(s):  
Kaizhi Liang ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Zhenpo Wang ◽  
Shangfeng Jiang
Keyword(s):  
Electric Vehicles ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Gradient Boosting ◽  
Estimation Model ◽  
Ohmic Resistance ◽  
Extreme Gradient Boosting ◽  
Battery Packs ◽  
Mean Square Errors

Accurate state-of-health (SOH) estimation for battery packs in electric vehicles (EVs) plays a pivotal role in preventing battery fault occurrence and extending their service life. In this paper, a novel internal ohmic resistance estimation method is proposed by combining electric circuit models and data-driven algorithms. Firstly, an improved recursive least squares (RLS) is used to estimate the internal ohmic resistance. Then, an automatic outlier identification method is presented to filter out the abnormal ohmic resistance estimated under different temperatures. Finally, the ohmic resistance estimation model is established based on the Extreme Gradient Boosting (XGBoost) regression algorithm and inputs of temperature and driving distance. The proposed model is examined based on test datasets. The root mean square errors (RMSEs) are less than 4 mΩ while the mean absolute percentage errors (MAPEs) are less than 6%. The results show that the proposed method is feasible and accurate, and can be implemented in real-world EVs.

scholarly journals High-Resolution Vegetation Mapping Using eXtreme Gradient Boosting Based on Extensive Features

2019 ◽  
Vol 11 (12) ◽  
pp. 1505 ◽  
Author(s):  
Heng Zhang ◽  
Anwar Eziz ◽  
Jian Xiao ◽  
Shengli Tao ◽  
Shaopeng Wang ◽  
...  
Keyword(s):  
New Zealand ◽  
High Resolution ◽  
Large Scale ◽  
Field Survey ◽  
Terrestrial Ecosystems ◽  
Google Earth ◽  
Gradient Boosting ◽  
Global Changes ◽  
Vegetation Map ◽  
Extreme Gradient Boosting

Accurate mapping of vegetation is a premise for conserving, managing, and sustainably using vegetation resources, especially in conditions of intensive human activities and accelerating global changes. However, it is still challenging to produce high-resolution multiclass vegetation map in high accuracy, due to the incapacity of traditional mapping techniques in distinguishing mosaic vegetation classes with subtle differences and the paucity of fieldwork data. This study created a workflow by adopting a promising classifier, extreme gradient boosting (XGBoost), to produce accurate vegetation maps of two strikingly different cases (the Dzungarian Basin in China and New Zealand) based on extensive features and abundant vegetation data. For the Dzungarian Basin, a vegetation map with seven vegetation types, 17 subtypes, and 43 associations was produced with an overall accuracy of 0.907, 0.801, and 0.748, respectively. For New Zealand, a map of 10 habitats and a map of 41 vegetation classes were produced with 0.946, and 0.703 overall accuracy, respectively. The workflow incorporating simplified field survey procedures outperformed conventional field survey and remote sensing based methods in terms of accuracy and efficiency. In addition, it opens a possibility of building large-scale, high-resolution, and timely vegetation monitoring platforms for most terrestrial ecosystems worldwide with the aid of Google Earth Engine and citizen science programs.

scholarly journals Exploiting Visual Features in Financial Time Series Prediction

2020 ◽  
Vol 14 (2) ◽  
pp. 61-76
Author(s):  
Adil Gürsel Karaçor ◽  
Turan Erman Erkan
Keyword(s):  
Financial Time Series ◽  
Time Series Prediction ◽  
Visual Features ◽  
Gradient Boosting ◽  
Support Vector ◽  
Foreign Exchange Rates ◽  
Extreme Gradient Boosting ◽  
Vector Machines ◽  
Direction Of Movement ◽  
Visual Properties

The possibility to enhance prediction accuracy for foreign exchange rates was investigated in two ways: first applying an outside the box approach to modeling price graphs by exploiting their visual properties, and secondly employing the most efficient methods to detect patterns to classify the direction of movement. The approach that exploits the visual properties of price graphs which make use of density regions along with high and low values describing the shape; hence, the authors propose the name ‘Finance Vision.' The data used in the predictive model consists of 1-hour past price values of 4 different currency pairs, between 2003 and 2016. Prediction performances of state-of-the-art methods; Extreme Gradient Boosting, Artificial Neural Network and Support Vector Machines are compared over the same data with the same sets of features. Results show that density based visual features contribute considerably to prediction performance.

scholarly journals Modeling of nitrogen solubility in normal alkanes using machine learning methods compared with cubic and PC-SAFT equations of state

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Ali Madani ◽  
Mohammad-Reza Mohammadi ◽  
Saeid Atashrouz ◽  
Ali Abedi ◽  
Abdolhossein Hemmati-Sarapardeh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Weight ◽  
Oil Recovery ◽  
Equations Of State ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Operating Pressure ◽  
Normal Alkanes ◽  
Light Gradient ◽  
Extreme Gradient Boosting

AbstractAccurate prediction of the solubility of gases in hydrocarbons is a crucial factor in designing enhanced oil recovery (EOR) operations by gas injection as well as separation, and chemical reaction processes in a petroleum refinery. In this work, nitrogen (N2) solubility in normal alkanes as the major constituents of crude oil was modeled using five representative machine learning (ML) models namely gradient boosting with categorical features support (CatBoost), random forest, light gradient boosting machine (LightGBM), k-nearest neighbors (k-NN), and extreme gradient boosting (XGBoost). A large solubility databank containing 1982 data points was utilized to establish the models for predicting N2 solubility in normal alkanes as a function of pressure, temperature, and molecular weight of normal alkanes over broad ranges of operating pressure (0.0212–69.12 MPa) and temperature (91–703 K). The molecular weight range of normal alkanes was from 16 to 507 g/mol. Also, five equations of state (EOSs) including Redlich–Kwong (RK), Soave–Redlich–Kwong (SRK), Zudkevitch–Joffe (ZJ), Peng–Robinson (PR), and perturbed-chain statistical associating fluid theory (PC-SAFT) were used comparatively with the ML models to estimate N2 solubility in normal alkanes. Results revealed that the CatBoost model is the most precise model in this work with a root mean square error of 0.0147 and coefficient of determination of 0.9943. ZJ EOS also provided the best estimates for the N2 solubility in normal alkanes among the EOSs. Lastly, the results of relevancy factor analysis indicated that pressure has the greatest influence on N2 solubility in normal alkanes and the N2 solubility increases with increasing the molecular weight of normal alkanes.

scholarly journals Random forest and extreme gradient boosting algorithms for streamflow modeling using vessel features, and tree-rings

2021 ◽  
Author(s):  
Hossein Sahour ◽  
Vahid Gholami ◽  
Javad Torkman ◽  
Mehdi Vazifedan ◽  
Sirwe Saeedi
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Tree Rings ◽  
Test Site ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Growing Seasons ◽  
Extreme Gradient Boosting ◽  
Streamflow Modeling

Abstract Monitoring temporal variation of streamflow is necessary for many water resources management plans, yet, such practices are constrained by the absence or paucity of data in many rivers around the world. Using a permanent river in the north of Iran as a test site, a machine learning framework was proposed to model the streamflow data in the three periods of growing seasons based on tree-rings and vessel features of the Zelkova carpinifolia species. First, full-disc samples were taken from 30 trees near the river, and the samples went through preprocessing, cross-dating, standardization, and time series analysis. Two machine learning algorithms, namely random forest (RF) and extreme gradient boosting (XGB), were used to model the relationships between dendrochronology variables (tree-rings and vessel features in the three periods of growing seasons) and the corresponding streamflow rates. The performance of each model was evaluated using statistical coefficients (coefficient of determination (R-squared), Nash-Sutcliffe efficiency (NSE), and root-mean-square error (NRMSE)). Findings demonstrate that consideration should be given to the XGB model in streamflow modeling given its apparent enhanced performance (R-squared: 0.87; NSE: 0.81; and NRMSE: 0.43) over the RF model (R-squared: 0.82; NSE: 0.71; and NRMSE: 0.52). Further, the results showed that the models perform better in modeling the normal and low flows compared to extremely high flows. Finally, the tested models were used to reconstruct the temporal streamflow during the past decades (1970–1981).

scholarly journals An Interpretable Extreme Gradient Boosting Model to Predict Ash Fusion Temperatures

Minerals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 487
Author(s):  
Maciej Rzychoń ◽  
Alina Żogała ◽  
Leokadia Róg
Keyword(s):  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Important Indicator ◽  
Upper Silesian Coal Basin ◽  
Proposed Model ◽  
Extreme Gradient Boosting ◽  
The Impact ◽  
Partial Dependence ◽  
Individual Input

The hemispherical temperature (HT) is the most important indicator representing ash fusion temperatures (AFTs) in the Polish industry to assess the suitability of coal for combustion as well as gasification purposes. It is important, for safe operation and energy saving, to know or to be able to predict value of this parameter. In this study a non-linear model predicting the HT value, based on ash oxides content for 360 coal samples from the Upper Silesian Coal Basin, was developed. The proposed model was established using the machine learning method—extreme gradient boosting (XGBoost) regressor. An important feature of models based on the XGBoost algorithm is the ability to determine the impact of individual input parameters on the predicted value using the feature importance (FI) technique. This method allowed the determination of ash oxides having the greatest impact on the projected HT. Then, the partial dependence plots (PDP) technique was used to visualize the effect of individual oxides on the predicted value. The results indicate that proposed model could estimate value of HT with high accuracy. The coefficient of determination (R2) of the prediction has reached satisfactory value of 0.88.

scholarly journals XGB+FM for Severe Convection Forecast and Factor Selection

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 321
Author(s):  
Zhiying Lu ◽  
Xudong Ding ◽  
Xin Li ◽  
Haopeng Wu ◽  
Xiaolei Sun
Keyword(s):  
Short Term Memory ◽  
Class Imbalance ◽  
Meteorological Factor ◽  
Radiosonde Data ◽  
Gradient Boosting ◽  
Feature Interaction ◽  
Support Vector ◽  
Observation Data ◽  
Severe Convection ◽  
Extreme Gradient Boosting

In the field of meteorology, radiosonde data and observation data are critical for analyzing regional meteorological characteristics. Because of the high false alarm rate, severe convection forecasting is still challenging. In addition, the existing methods are difficult to use to capture the interaction of meteorological factors at the same time. In this research, a cascade of extreme gradient boosting (XGBoost) for feature transformation and a factorization machine (FM) for second-order feature interaction to capture the nonlinear interaction—XGB+FM—is proposed. An attention-based bidirectional long short-term memory (Att-Bi-LSTM) network is proposed to impute the missing data of meteorological observation stations. The problem of class imbalance is resolved by the support vector machines–synthetic minority oversampling technique (SVM-SMOTE), in which two oversampling strategies based on the support vector discrimination mechanism are proposed. It is proven that the method is effective, and the threat score (TS) is 7.27~14.28% higher than other methods. Moreover, we propose the meteorological factor selection method based on XGB+FM and improve the forecast accuracy, which is one of our contributions, as well as the forecast system.

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